class_docstr: Construct an NLLB tokenizer.
  
  Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
  [SentencePiece](https://github.com/google/sentencepiece).
  
  The tokenization method is `<tokens> <eos> <language code>` for source language documents, and `<language code>
  <tokens> <eos>` for target language documents.
  
  Examples:
  
  ```python
  >>> from transformers import NllbTokenizer
  
  >>> tokenizer = NllbTokenizer.from_pretrained(
  ...     "facebook/nllb-200-distilled-600M", src_lang="eng_Latn", tgt_lang="fra_Latn"
  ... )
  >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
  >>> expected_translation_french = "Le chef de l'ONU affirme qu'il n'y a pas de solution militaire en Syrie."
  >>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_french, return_tensors="pt")
  ```
  
  Args:
      vocab_file (`str`):
          Path to the vocabulary file.
      bos_token (`str`, *optional*, defaults to `"<s>"`):
          The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
  
          <Tip>
  
          When building a sequence using special tokens, this is not the token that is used for the beginning of
          sequence. The token used is the `cls_token`.
  
          </Tip>
  
      eos_token (`str`, *optional*, defaults to `"</s>"`):
          The end of sequence token.
  
          <Tip>
  
          When building a sequence using special tokens, this is not the token that is used for the end of sequence.
          The token used is the `sep_token`.
  
          </Tip>
  
      sep_token (`str`, *optional*, defaults to `"</s>"`):
          The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
          sequence classification or for a text and a question for question answering. It is also used as the last
          token of a sequence built with special tokens.
      cls_token (`str`, *optional*, defaults to `"<s>"`):
          The classifier token which is used when doing sequence classification (classification of the whole sequence
          instead of per-token classification). It is the first token of the sequence when built with special tokens.
      unk_token (`str`, *optional*, defaults to `"<unk>"`):
          The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
          token instead.
      pad_token (`str`, *optional*, defaults to `"<pad>"`):
          The token used for padding, for example when batching sequences of different lengths.
      mask_token (`str`, *optional*, defaults to `"<mask>"`):
          The token used for masking values. This is the token used when training this model with masked language
          modeling. This is the token which the model will try to predict.
      tokenizer_file (`str`, *optional*):
          The path to a tokenizer file to use instead of the vocab file.
      src_lang (`str`, *optional*):
          The language to use as source language for translation.
      tgt_lang (`str`, *optional*):
          The language to use as target language for translation.
      sp_model_kwargs (`Dict[str, str]`):
          Additional keyword arguments to pass to the model initialization.
code: def _switch_to_target_mode(self):
      return self.set_tgt_lang_special_tokens(self.tgt_lang)
  
imports: import os
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple
  import sentencepiece
  from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
  from ...utils import logging
label_desc: Use image node_id 19 for calling the NllbTokenizer obj's underlying member method code with example usage: obj._switch_to_target_mode() and returns: self